Geos Institute

Rapid Assessment of the Yale Framework and Adaptation Blueprint for the North America Pacific Coastal Rainforest

For the Pacific Coastal Rainforests, the framework helped identify where terrestrial rainforest ecosystems can be successfully maintained for resilience and resistance to climate change due to microsite features (microrefugia). Other areas, while still important, might be more successfully managed for transition to a different ecological state as emphasized in the framework. The spatially explicit nature of this study allowed us to identify intact areas to conserve, fragmented ones to restore, private lands to prioritize for conservation, and public lands to prioritize for changes in land management plans, adding on-the-ground conservation opportunities without major investments.

We used downscaled general circulation models, species distribution models, vegetation models, and other datasets to test all six of the Yale framework objectives and 15 of 18 framework adaptation cells.We applied the framework to the Pacific coastal rainforest region because of its global conservation importance, lack of robust conservation and adaptation strategies, and interest from partnering agencies and organizations. We used agreement among multiple models to determine levels of certainty in forecasting climate change effects to rainforest assemblages, focal species, and ecosystem processes.

Objectives

Use framework elements to provide an integrated assessment of spatially explicit adaptation opportunities in the Pacific Coastal Rainforest.

Evaluate the framework’s efficacy to adaptation planning for four adaptation blueprints.

The Geos Institute project used a Maxent rainforest distribution model to identify baseline locations and projected climatic shifts in temperate rainforest assemblages and focal species from northern California to southern Alaska. This information provides an assessment of future patterns in terrestrial rainforest habitat and associated species,which the North Pacific LCC could use as a decision support tool to help identify priority locations for applied science to inform conservation and adaptive management. The Geos Institute also used downscaled temperature and precipitation data as inputs to the MC1 dynamic vegetation model in order to identify areas of relative climate stability and instability at the ecosystem scale.

Climate envelope modeling was also used to provide projections for future range distributions of 14 focal species with the goal of anticipating and protecting the locations that will meet the needs of these species under future conditions. The modeled climate niche approaches are data intensive and depend on reliable plot data, which was secured for 8 focal conifer species of commercial value (Sitka spruce, Western and mountain hemlock, Pacific silver and grand fir, Alaska yellow-cedar, Western redcedar,and coast redwood), 2 epiphytic lichens (witch’s hair, lettuce lichen) important in nutrient cycling and forage for wildlife, 2 threatened bird species(northern spotted owl, marbled murrelet), and a culturally significant species(Sitka black-tailed deer).

We identified areas likely to retain stable climatic conditions throughout and grouped them by elevation bands (“enduring stage”),which were disproportionately distributed throughout the region, including a noticeable gap in the seasonal rainforest zone in the south. We also identified intact, old forests with relatively stable climate as likely “climatic refugia”but most of these areas occurred to the north. Projected increases in fire(seasonal and warm zones) occur under some models (high uncertainty) along with reductions in forest carbon pools (ecosystem processes).

The types of tools used to construct an adaptation blueprint for this region and their application to the Yale framework included:

Species distribution models – constructed predicted baseline species distributions for the 13 focal species using point datasets in a Maxent presence only model combined with Worldclim datasets and projected future distributions using GCM models.

We used presence-only models to map focal species distributions that were obtained from various databases and regional specialists (summary table). To minimize geographical errors, we compared predicted focal species distributions to available range maps and revised them based on review of regional experts. We used Maxent to predict current and future potential species distributions for each focal species and reduced the19 variable climate predictor dataset for focal species by consulting jackknife output tables from initial model runs, leaving only those climate predictors that explained current climate niches. A 30% model training dataset of the applied localities randomly was permutated in each model run and we activated the “fade by clamping” option in Maxent to mitigate clamping issues arising from these data.

We identified intact old-growth rainforests, using a 2001 and 2006 old-growth forest intactness map available from databasin.org to identify potential sustainable source populations for future tree dispersal. For each focal conifer species,we mapped where species persistence through 2080 overlapped with high intactness values. These were identified as “source” areas (climate refugia)for each species. To identify “target” areas, we mapped areas that had low intactness but suitable climate in 2080.

We used a variety of spatially explicit analyses and datasets to test the Yale framework, including applying statistically downscaled GCMs (CCCMA-CGCM2 (Canadian Centre for Climate Modelling and Analysis), CSIRO-MK2 (Australia’s Commonwealth Scientific and Industrial Research Organisation), and HADCM3 (Hadley Centre for Climate Prediction) that were run for two emissions scenarios (A1B and A2A) and analyzed at 1-km resolution. We used the MC1 dynamic vegetation model to assess potential stability of dominant types of vegetation, weighted by model agreement, by comparing outputs from three GCMs: Hadley (HadCM3), MIROC, and CSIRO that were downscaled to 8-km resolution. We assessed vegetation stability by comparing the dominant type of vegetation predicted to be supported under modeled baseline conditions (1961-1990) to that predicted under future conditions for two time periods (2035-45 and 2075-85). We also used MC1 output to assess changes in wildfire and carbon stored in vegetation from the baseline period through 2075-85 across the study area. We overlaid digital elevation models and protected areas (Global Forest Watch Canada) onto stable areas to generally assess degree of representation by elevation and percent coverage of stable intact areas in protected areas.

At broader spatial scales, projected climatic conditions become more favorable north (perhumid,subpolar zones) for temperate cool mixed and temperate broadleaf woodland than the baseline (1950-2000) maritime evergreen forests, and more favorable for maritime evergreen needleleaf, temperate evergreen needleleaf and temperate broadleaf than the baseline subalpine vegetation by 2075. In the south(seasonal, warm zones), the climate becomes more suited for subtropical mixed forests along the coast for than the baseline maritime evergreen needleleaf by2075. Most focal species had continued climate niche persistence but showed substantial reductions in baseline (1950-1991) climate niche by 2080 primarily in southern and coastal zones with potential gains in climate niche associated with increasing latitude (Alaska, BC) and elevation. Rainforest conifers therefore may contract to the Olympic Peninsula with extensive loss of the climate niche southward, particularly the near loss of the climate niche of coast redwood (Sequoia sempervirens).

Most of the 8focal tree species were likely to experience a reduction in suitable climate niche in southern and coastal areas with potential gains in climate niche associated with increasing latitude and elevation. Broad changes in plant communities (based on MC1 models) were likely with climatic conditions shifting to favor deciduous trees northward and shrub, woodland, and subtropical types southward based on 3 downscaled climate models (CSIRO, Hadley, MIROC; A2 emissions scenario). Using MC1 models, we identified areas most likely to retain stable climatic conditions where dominant types of vegetation were also most likely to persist. Intact old forests with relatively stable climates were identified for focal tree species as potential climatic refugia. Target space, consisting of areas climatically suitable for focal species now and in the future, yet not necessarily intact, was also identified. In addition, projected increases in fire (southern region) were likely under some models along with reductions in carbon storage.

The resulting maps present the future, dominant vegetation types occurring across the landscape and can be used to infer likely impacts to and shifts in wildlife species and biodiversity dependent on particular habitats or vegetation types.Because this information is relayed at an 8km scale, it will likely help inform federal or state level planning efforts. For example, the U.S. Forest Service can use this information during their Forest Plan Revision process to prioritize areas for protection that will meet the habitat needs of biodiversity under future conditions and in its climate scorecard process(which requires vulnerability assessments).

Utilizing these types of correlative bioclimatic models can be very useful for landscape-level planners because adaptation strategies can be designed and implemented cross-boundary at multiple scales. However, these models can be time and resource-intensive and neglect interacting non-climatic stressors such as invasive or competing species. The use of climate envelope models is controversial and downscaled climate models can give varied results for the same species. However, we addressed model uncertainty throughout this effort in a way that should make the results scientifically defensible.

In general, managers interested in planning for climate change should:(1) protect intact areas where climate and vegetation are likely to remain stable; (2) reduce non-climate stressors from land-use actions; (3) protect forested areas of high carbon density because of dual benefits to mitigation and adaptation; and (4) restore degraded climatically stable areas by also connecting them to intact stable areas to facilitate climate-forced species migrations. Notably, while 30% of the study area is in legally protected areas,only 14% of protected areas are projected to remain climatically stable and just 4% of these include remaining late-seral forests. Decision support tools for reserve design should prepare for a shifting climate by further assessing the degree of representativeness of stable areas in expanding the reserve network so it is more robust to climate change.